{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Health risk analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import csv\n",
    "from pandas import read_excel\n",
    "from scipy import stats\n",
    "from scipy.stats import spearmanr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>de</th>\n",
       "      <th>dk</th>\n",
       "      <th>fr</th>\n",
       "      <th>it</th>\n",
       "      <th>nl</th>\n",
       "      <th>se</th>\n",
       "      <th>sp</th>\n",
       "      <th>us</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>2.097183</td>\n",
       "      <td>2.097183</td>\n",
       "      <td>2.097183</td>\n",
       "      <td>2.097183</td>\n",
       "      <td>2.097183</td>\n",
       "      <td>2.097183</td>\n",
       "      <td>2.097183</td>\n",
       "      <td>2.097183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phi</th>\n",
       "      <td>0.304140</td>\n",
       "      <td>0.304140</td>\n",
       "      <td>0.304140</td>\n",
       "      <td>0.304140</td>\n",
       "      <td>0.304140</td>\n",
       "      <td>0.304140</td>\n",
       "      <td>0.304140</td>\n",
       "      <td>0.304140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>psi</th>\n",
       "      <td>0.168902</td>\n",
       "      <td>0.168902</td>\n",
       "      <td>0.168902</td>\n",
       "      <td>0.168902</td>\n",
       "      <td>0.168902</td>\n",
       "      <td>0.168902</td>\n",
       "      <td>0.168902</td>\n",
       "      <td>0.168902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_h1</th>\n",
       "      <td>-1.296156</td>\n",
       "      <td>-1.601068</td>\n",
       "      <td>-1.094451</td>\n",
       "      <td>-0.715889</td>\n",
       "      <td>-1.264525</td>\n",
       "      <td>-1.525023</td>\n",
       "      <td>-0.005673</td>\n",
       "      <td>-0.967380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_h2</th>\n",
       "      <td>3.953905</td>\n",
       "      <td>4.273184</td>\n",
       "      <td>3.697940</td>\n",
       "      <td>3.867593</td>\n",
       "      <td>3.998342</td>\n",
       "      <td>4.310607</td>\n",
       "      <td>3.394047</td>\n",
       "      <td>3.487244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eta</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tfp</th>\n",
       "      <td>1.008961</td>\n",
       "      <td>1.255176</td>\n",
       "      <td>0.923597</td>\n",
       "      <td>0.638877</td>\n",
       "      <td>1.009820</td>\n",
       "      <td>0.788306</td>\n",
       "      <td>0.810865</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.846861</td>\n",
       "      <td>0.887615</td>\n",
       "      <td>0.614844</td>\n",
       "      <td>0.712935</td>\n",
       "      <td>0.698165</td>\n",
       "      <td>0.843816</td>\n",
       "      <td>0.650287</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                de        dk        fr        it        nl        se  \\\n",
       "name                                                                   \n",
       "sigma     2.097183  2.097183  2.097183  2.097183  2.097183  2.097183   \n",
       "beta      0.970000  0.970000  0.970000  0.970000  0.970000  0.970000   \n",
       "phi       0.304140  0.304140  0.304140  0.304140  0.304140  0.304140   \n",
       "psi       0.168902  0.168902  0.168902  0.168902  0.168902  0.168902   \n",
       "delta_h1 -1.296156 -1.601068 -1.094451 -0.715889 -1.264525 -1.525023   \n",
       "delta_h2  3.953905  4.273184  3.697940  3.867593  3.998342  4.310607   \n",
       "eta       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "tfp       1.008961  1.255176  0.923597  0.638877  1.009820  0.788306   \n",
       "price     0.846861  0.887615  0.614844  0.712935  0.698165  0.843816   \n",
       "risk      0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "                sp        us  \n",
       "name                          \n",
       "sigma     2.097183  2.097183  \n",
       "beta      0.970000  0.970000  \n",
       "phi       0.304140  0.304140  \n",
       "psi       0.168902  0.168902  \n",
       "delta_h1 -0.005673 -0.967380  \n",
       "delta_h2  3.394047  3.487244  \n",
       "eta       0.000000  0.000000  \n",
       "tfp       0.810865  1.000000  \n",
       "price     0.650287  1.000000  \n",
       "risk      0.000000  0.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_pickle('../estimation/output/params_ref_us.pkl')\n",
    "pars = df.loc[:,'value'].to_frame()\n",
    "pars.columns = ['us']\n",
    "countries = ['de','dk','fr','it','nl','se','sp','us']\n",
    "for c in countries:\n",
    "    df = pd.read_pickle('../estimation/output/params_ref_'+c+'.pkl')\n",
    "    pars[c] = df.loc[:,'value']\n",
    "pars = pars[countries]\n",
    "pars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulated Moments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cshare</th>\n",
       "      <th>mshare</th>\n",
       "      <th>gb</th>\n",
       "      <th>gg</th>\n",
       "      <th>h</th>\n",
       "      <th>g_q2</th>\n",
       "      <th>g_q3</th>\n",
       "      <th>g_q4</th>\n",
       "      <th>tfp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>0.683222</td>\n",
       "      <td>0.142521</td>\n",
       "      <td>0.227541</td>\n",
       "      <td>0.969140</td>\n",
       "      <td>0.880597</td>\n",
       "      <td>1.142220</td>\n",
       "      <td>1.231196</td>\n",
       "      <td>1.301280</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.747475</td>\n",
       "      <td>0.102762</td>\n",
       "      <td>0.488755</td>\n",
       "      <td>0.980820</td>\n",
       "      <td>0.962245</td>\n",
       "      <td>1.022251</td>\n",
       "      <td>1.037385</td>\n",
       "      <td>1.046837</td>\n",
       "      <td>0.823013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>0.776568</td>\n",
       "      <td>0.088634</td>\n",
       "      <td>0.427615</td>\n",
       "      <td>0.986063</td>\n",
       "      <td>0.968432</td>\n",
       "      <td>1.014438</td>\n",
       "      <td>1.025553</td>\n",
       "      <td>1.035745</td>\n",
       "      <td>0.900990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>0.769039</td>\n",
       "      <td>0.108333</td>\n",
       "      <td>0.500298</td>\n",
       "      <td>0.975226</td>\n",
       "      <td>0.952821</td>\n",
       "      <td>1.057299</td>\n",
       "      <td>1.082895</td>\n",
       "      <td>1.087157</td>\n",
       "      <td>0.732520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>0.710800</td>\n",
       "      <td>0.081824</td>\n",
       "      <td>0.272530</td>\n",
       "      <td>0.979091</td>\n",
       "      <td>0.928765</td>\n",
       "      <td>1.036969</td>\n",
       "      <td>1.066782</td>\n",
       "      <td>1.100864</td>\n",
       "      <td>0.733720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>0.750653</td>\n",
       "      <td>0.081014</td>\n",
       "      <td>0.641872</td>\n",
       "      <td>0.981654</td>\n",
       "      <td>0.972211</td>\n",
       "      <td>1.021743</td>\n",
       "      <td>1.029750</td>\n",
       "      <td>1.031810</td>\n",
       "      <td>0.870789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>0.716410</td>\n",
       "      <td>0.086997</td>\n",
       "      <td>0.384000</td>\n",
       "      <td>0.986575</td>\n",
       "      <td>0.966213</td>\n",
       "      <td>1.006930</td>\n",
       "      <td>1.012854</td>\n",
       "      <td>1.020789</td>\n",
       "      <td>0.790343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <td>0.766439</td>\n",
       "      <td>0.074915</td>\n",
       "      <td>0.433196</td>\n",
       "      <td>0.966427</td>\n",
       "      <td>0.928078</td>\n",
       "      <td>1.103412</td>\n",
       "      <td>1.127606</td>\n",
       "      <td>1.138246</td>\n",
       "      <td>0.643379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cshare    mshare        gb        gg         h      g_q2      g_q3  \\\n",
       "US  0.683222  0.142521  0.227541  0.969140  0.880597  1.142220  1.231196   \n",
       "DE  0.747475  0.102762  0.488755  0.980820  0.962245  1.022251  1.037385   \n",
       "DK  0.776568  0.088634  0.427615  0.986063  0.968432  1.014438  1.025553   \n",
       "FR  0.769039  0.108333  0.500298  0.975226  0.952821  1.057299  1.082895   \n",
       "IT  0.710800  0.081824  0.272530  0.979091  0.928765  1.036969  1.066782   \n",
       "NL  0.750653  0.081014  0.641872  0.981654  0.972211  1.021743  1.029750   \n",
       "SE  0.716410  0.086997  0.384000  0.986575  0.966213  1.006930  1.012854   \n",
       "SP  0.766439  0.074915  0.433196  0.966427  0.928078  1.103412  1.127606   \n",
       "\n",
       "        g_q4       tfp  \n",
       "US  1.301280  1.000000  \n",
       "DE  1.046837  0.823013  \n",
       "DK  1.035745  0.900990  \n",
       "FR  1.087157  0.732520  \n",
       "IT  1.100864  0.733720  \n",
       "NL  1.031810  0.870789  \n",
       "SE  1.020789  0.790343  \n",
       "SP  1.138246  0.643379  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading results from estimation\n",
    "momsim = pd.read_pickle('output/moms_sim.pkl')\n",
    "momsim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DALYs \n",
    "## Data from: Global Burden of Disease Collaborative Network. \n",
    "### Global Burden of Disease Study 2017 (GBD 2017) Results. \n",
    "Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.\n",
    "\n",
    "\n",
    "### DALY: Definition\n",
    "\n",
    "One DALY can be thought of as one lost year of \"healthy\" life. The sum of these DALYs across the population, or the burden of disease, can be thought of as a measurement of the gap between current health status and an ideal health situation where the entire population lives to an advanced age, free of disease and disability.\n",
    "\n",
    "DALYs for a disease or health condition are calculated as the sum of the Years of Life Lost (YLL) due to premature mortality in the population and the Years Lost due to Disability (YLD) for people living with the health condition or its consequences\n",
    "\n",
    "### DALY: calculation\n",
    "\n",
    "DALYs for a disease or health condition are calculated as the sum of the Years of Life Lost (YLL) due to premature mortality in the population and the Years Lost due to Disability (YLD) for people living with the health condition or its consequences:\n",
    "Calculation\n",
    "$$\n",
    "DALY = YLL + YLD \n",
    "$$\n",
    "with  \n",
    "\n",
    "1/ $YLL = N \\times L$  where $N = $ number of deaths $L =$ standard life expectancy at age of death in years, \n",
    "\n",
    "2/ $YLD = P \\times DW$ where $P =$ number of prevalent cases and $DW =$ disability weight\n",
    "\n",
    "### Interpretation\n",
    "\n",
    "The DALYs give an indicator on the probability to be in bad health or to die, and this for each risky behavior. Thus DALYs can be related  to the probability to remain in bad health, being previouly in bad health because DALYs account for the number of  lost years in good health."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = '../data_sources/xlsx_files/DALY_my_sample.xlsx'\n",
    "my_sheet  = 'Feuil2000'    # sheet name\n",
    "data_daly2000 = read_excel(file_name, sheet_name = my_sheet)\n",
    "my_sheet  = 'Feuil2017'    # sheet name\n",
    "data_daly2017 = read_excel(file_name, sheet_name = my_sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_col0 = ['Unnamed: 0',\n",
    "            'Air pollution (outdoor & indoor)\\nDALYs','Child wasting\\nDALYs','Child stunting\\nDALYs',\n",
    "            'Secondhand smoke\\nDALYs','Unsafe sanitation\\nDALYs','Unsafe water source\\nDALYs',\n",
    "            'Low physical activity\\nDALYs',\n",
    "            'High cholesterol\\nDALYs','Non-exclusive breastfeeding\\nDALYs',\n",
    "            'Outdoor air pollution\\nDALYs','Indoor air pollution\\nDALYs',\n",
    "            'Drug use\\nDALYs','Diet low in fruits\\nDALYs',\n",
    "            'Iron deficiency\\nDALYs','Zinc deficiency\\nDALYs','Diet high in salt\\nDALYs',\n",
    "            'Diet low in vegetables\\nDALYs','Vitamin A deficiency\\nDALYs','Smoking\\nDALYs',\n",
    "            'High blood pressure\\nDALYs','High blood sugar\\nDALYs','Obesity\\nDALYs',\n",
    "            'Total'] \n",
    "\n",
    "name_col = ['Air pollution (outdoor & indoor)\\nDALYs','Child wasting\\nDALYs','Child stunting\\nDALYs',\n",
    "            'Secondhand smoke\\nDALYs','Unsafe sanitation\\nDALYs','Unsafe water source\\nDALYs',\n",
    "            'Low physical activity\\nDALYs',\n",
    "            'High cholesterol\\nDALYs','Non-exclusive breastfeeding\\nDALYs',\n",
    "            'Outdoor air pollution\\nDALYs','Indoor air pollution\\nDALYs',\n",
    "            'Drug use\\nDALYs','Diet low in fruits\\nDALYs',\n",
    "            'Iron deficiency\\nDALYs','Zinc deficiency\\nDALYs','Diet high in salt\\nDALYs',\n",
    "            'Diet low in vegetables\\nDALYs','Vitamin A deficiency\\nDALYs','Smoking\\nDALYs',\n",
    "            'High blood pressure\\nDALYs','High blood sugar\\nDALYs','Obesity\\nDALYs',\n",
    "            'Total'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Population size 2005 (Million)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2005\n",
    "PopL2005 = np.array([81.60, 5.42, 61.12, 58.28, 16.37, 9.04, 44.02, 294.99])\n",
    "PopL2000 = np.array([81.40, 5.34, 59.02, 56.69, 15.93, 8.88, 40.83, 281.71])\n",
    "PopL2017 = np.array([82.66, 5.73, 64.84, 60.67, 17.02, 9.90, 46.65, 325.08])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data DALYs per inhabitant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = ['de','dk','fr','it','nl','se','sp','us']\n",
    "\n",
    "daly2017 = pd.DataFrame(index=countries,columns=name_col)\n",
    "for ii in range(23): #23\n",
    "    yy = name_col0[ii+1]\n",
    "    for jj in range(8):\n",
    "        cc = countries[jj]\n",
    "        daly2017.loc[cc,yy] = data_daly2017.loc[jj,yy]/(PopL2017[jj]*1000000)\n",
    "\n",
    "daly2000 = pd.DataFrame(index=countries,columns=name_col)\n",
    "for ii in range(23): #23\n",
    "    yy = name_col0[ii+1]\n",
    "    for jj in range(8):\n",
    "        cc = countries[jj]\n",
    "        daly2000.loc[cc,yy] = data_daly2000.loc[jj,yy]/(PopL2000[jj]*1000000)\n",
    "\n",
    "daly_mean = (daly2000+daly2017)/2\n",
    "\n",
    "tables_daly = pd.DataFrame(index=name_col,columns=countries)\n",
    "for cc in range(8):\n",
    "    tt = countries[cc]\n",
    "    tables_daly.loc[:,tt] = daly_mean.loc[tt,name_col[0:23]]\n",
    "\n",
    "tables_daly_us = pd.DataFrame(index=name_col,columns=countries)\n",
    "tables_daly_us = tables_daly.sort_values('us',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>de</th>\n",
       "      <th>dk</th>\n",
       "      <th>fr</th>\n",
       "      <th>it</th>\n",
       "      <th>nl</th>\n",
       "      <th>se</th>\n",
       "      <th>sp</th>\n",
       "      <th>us</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Smoking</th>\n",
       "      <td>0.044274</td>\n",
       "      <td>0.052392</td>\n",
       "      <td>0.032866</td>\n",
       "      <td>0.036667</td>\n",
       "      <td>0.046289</td>\n",
       "      <td>0.033835</td>\n",
       "      <td>0.037064</td>\n",
       "      <td>0.036666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Obesity</th>\n",
       "      <td>0.026268</td>\n",
       "      <td>0.021374</td>\n",
       "      <td>0.016101</td>\n",
       "      <td>0.021992</td>\n",
       "      <td>0.019666</td>\n",
       "      <td>0.021249</td>\n",
       "      <td>0.011303</td>\n",
       "      <td>0.032754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diet</th>\n",
       "      <td>0.016163</td>\n",
       "      <td>0.011432</td>\n",
       "      <td>0.010585</td>\n",
       "      <td>0.011232</td>\n",
       "      <td>0.009962</td>\n",
       "      <td>0.016481</td>\n",
       "      <td>0.008656</td>\n",
       "      <td>0.016017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <td>0.003797</td>\n",
       "      <td>0.004668</td>\n",
       "      <td>0.003723</td>\n",
       "      <td>0.002833</td>\n",
       "      <td>0.002628</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>0.004055</td>\n",
       "      <td>0.0158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pollution</th>\n",
       "      <td>0.010275</td>\n",
       "      <td>0.009832</td>\n",
       "      <td>0.006065</td>\n",
       "      <td>0.009504</td>\n",
       "      <td>0.009459</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>0.00748</td>\n",
       "      <td>0.008198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phys. Act.</th>\n",
       "      <td>0.005044</td>\n",
       "      <td>0.004065</td>\n",
       "      <td>0.002907</td>\n",
       "      <td>0.004079</td>\n",
       "      <td>0.003228</td>\n",
       "      <td>0.004911</td>\n",
       "      <td>0.003388</td>\n",
       "      <td>0.004131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>0.105822</td>\n",
       "      <td>0.103764</td>\n",
       "      <td>0.072245</td>\n",
       "      <td>0.086306</td>\n",
       "      <td>0.091232</td>\n",
       "      <td>0.085412</td>\n",
       "      <td>0.071945</td>\n",
       "      <td>0.113567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  de        dk        fr        it        nl        se  \\\n",
       "Smoking     0.044274  0.052392  0.032866  0.036667  0.046289  0.033835   \n",
       "Obesity     0.026268  0.021374  0.016101  0.021992  0.019666  0.021249   \n",
       "Diet        0.016163  0.011432  0.010585  0.011232  0.009962  0.016481   \n",
       "Drug        0.003797  0.004668  0.003723  0.002833  0.002628  0.003397   \n",
       "Pollution   0.010275  0.009832  0.006065  0.009504  0.009459   0.00554   \n",
       "Phys. Act.  0.005044  0.004065  0.002907  0.004079  0.003228  0.004911   \n",
       "Total       0.105822  0.103764  0.072245  0.086306  0.091232  0.085412   \n",
       "\n",
       "                  sp        us  \n",
       "Smoking     0.037064  0.036666  \n",
       "Obesity     0.011303  0.032754  \n",
       "Diet        0.008656  0.016017  \n",
       "Drug        0.004055    0.0158  \n",
       "Pollution    0.00748  0.008198  \n",
       "Phys. Act.  0.003388  0.004131  \n",
       "Total       0.071945  0.113567  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_s   = ['Smoking','Obesity','Diet', 'Drug','Pollution','Phys. Act.','Total']\n",
    "tables_s = pd.DataFrame(index=name_s,columns=countries)\n",
    "for cc in range(8):\n",
    "    tt = countries[cc]\n",
    "    tables_s.loc['Smoking',tt]    = tables_daly_us.loc[tables_daly_us.index[1],tt]\n",
    "    tables_s.loc['Obesity',tt]    = tables_daly_us.loc[tables_daly_us.index[2],tt]\n",
    "    tables_s.loc['Diet',tt]       = tables_daly_us.loc[tables_daly_us.index[9],tt] + tables_daly_us.loc[tables_daly_us.index[10],tt] + tables_daly_us.loc[tables_daly_us.index[11],tt]\n",
    "    tables_s.loc['Drug',tt]       = tables_daly_us.loc[tables_daly_us.index[5],tt]\n",
    "    tables_s.loc['Pollution',tt]  = tables_daly_us.loc[tables_daly_us.index[7],tt]\n",
    "    tables_s.loc['Phys. Act.',tt] = tables_daly_us.loc[tables_daly_us.index[12],tt]    \n",
    "    tables_s.loc['Total',tt]      = tables_s.loc['Smoking',tt] + tables_s.loc['Obesity',tt] + tables_s.loc['Diet',tt] + tables_s.loc['Drug',tt] + tables_s.loc['Pollution',tt] + tables_s.loc['Phys. Act.',tt]\n",
    "tables_s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figures where the cost of each risky behaviors is measured as the number of lost years "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "names_s = ['','Smoking','Obesity','Diet', 'Drug','Pollution','Phys. Act.']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "xxx = np.arange(len(name_s[0:-1]))\n",
    "www = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(len(countries))\n",
    "countries_plot = ['','de','dk','fr','it','nl','se','sp','us']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/h9/sn9_m3953j93s5pv8q78dfvh0000gn/T/ipykernel_62210/4149102491.py:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(names_ss)\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "names_ss = ['','','Smoking','', 'Obesity','', 'Drug','', 'Phys. Act.']\n",
    "xxxs = np.arange(4)\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(xxxs-.3,70/(1-tables_s.loc[['Smoking','Obesity', 'Drug','Phys. Act.'],'us'])-70, width=www,color='black',label='us')\n",
    "ax.bar(xxxs-.2,70/(1-tables_s.loc[['Smoking','Obesity', 'Drug','Phys. Act.'],'de'])-70, width=www,color='dimgrey'  ,edgecolor='black',label='de')\n",
    "ax.bar(xxxs-.1,70/(1-tables_s.loc[['Smoking','Obesity', 'Drug','Phys. Act.'],'dk'])-70, width=www,color='dimgrey'  ,edgecolor='black',label='dk')\n",
    "ax.bar(xxxs,   70/(1-tables_s.loc[['Smoking','Obesity', 'Drug','Phys. Act.'],'fr'])-70, width=www,color='grey'     ,edgecolor='black',label='fr')\n",
    "ax.bar(xxxs+.1,70/(1-tables_s.loc[['Smoking','Obesity', 'Drug','Phys. Act.'],'it'])-70, width=www,color='darkgrey' ,edgecolor='black',label='it')\n",
    "ax.bar(xxxs+.2,70/(1-tables_s.loc[['Smoking','Obesity', 'Drug','Phys. Act.'],'nl'])-70, width=www,color='darkgrey' ,edgecolor='black',label='nl')\n",
    "ax.bar(xxxs+.3,70/(1-tables_s.loc[['Smoking','Obesity', 'Drug','Phys. Act.'],'se'])-70, width=www,color='lightgrey',edgecolor='black',label='se')\n",
    "ax.bar(xxxs+.4,70/(1-tables_s.loc[['Smoking','Obesity', 'Drug','Phys. Act.'],'sp'])-70, width=www,color='whitesmoke',edgecolor='black',label='sp')\n",
    "ax.set_xticklabels(names_ss)\n",
    "ax.set_ylabel('# of years lost')\n",
    "ax.legend()\n",
    "fig.tight_layout()\n",
    "plt.savefig('../figures/fig_c2_DALYs_details.eps')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mesures of risky behaviors\n",
    "These measures account for the intensity of risky behaviors in each country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>de</th>\n",
       "      <th>dk</th>\n",
       "      <th>fr</th>\n",
       "      <th>it</th>\n",
       "      <th>nl</th>\n",
       "      <th>se</th>\n",
       "      <th>sp</th>\n",
       "      <th>us</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Obesity</th>\n",
       "      <td>0.196425</td>\n",
       "      <td>0.165716</td>\n",
       "      <td>0.170822</td>\n",
       "      <td>0.276495</td>\n",
       "      <td>0.198036</td>\n",
       "      <td>0.197081</td>\n",
       "      <td>0.170931</td>\n",
       "      <td>0.328623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ever smoke</th>\n",
       "      <td>0.359872</td>\n",
       "      <td>0.402399</td>\n",
       "      <td>0.492596</td>\n",
       "      <td>0.322084</td>\n",
       "      <td>0.357995</td>\n",
       "      <td>0.333276</td>\n",
       "      <td>0.526519</td>\n",
       "      <td>0.578711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No Phys.Act</th>\n",
       "      <td>0.050765</td>\n",
       "      <td>0.027652</td>\n",
       "      <td>0.055629</td>\n",
       "      <td>0.103732</td>\n",
       "      <td>0.163662</td>\n",
       "      <td>0.088618</td>\n",
       "      <td>0.041033</td>\n",
       "      <td>0.099483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   de        dk        fr        it        nl        se  \\\n",
       "Obesity      0.196425  0.165716  0.170822  0.276495  0.198036  0.197081   \n",
       "Ever smoke   0.359872  0.402399  0.492596  0.322084  0.357995  0.333276   \n",
       "No Phys.Act  0.050765  0.027652  0.055629  0.103732  0.163662  0.088618   \n",
       "\n",
       "                   sp        us  \n",
       "Obesity      0.170931  0.328623  \n",
       "Ever smoke   0.526519  0.578711  \n",
       "No Phys.Act  0.041033  0.099483  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data RF\n",
    "nameRF = ['Obesity','Ever smoke','No Phys.Act']\n",
    "dataRF = np.array([\n",
    "[.1964254, .3598716, .0507653],\n",
    "[.1657165,.4023989,.0276525],\n",
    "[.1708216,.4925962,.0556293],\n",
    "[.2764948,.3220845,.1037317],\n",
    "[.1980363,.3579953,.1636622],\n",
    "[.1970815,.3332759,.0886183],\n",
    "[.1709308,.5265188,.0410328],\n",
    "[.3286226,.5787106,.0994831]])\n",
    "tableRF = pd.DataFrame(index=nameRF,columns=countries)\n",
    "for cc in range(8):\n",
    "    tt = countries[cc]\n",
    "    tableRF.loc[:,tt] = dataRF[cc,:]\n",
    "tableRF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>Obesity</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Child Obesity</th>\n",
       "      <th>Lack of support MA</th>\n",
       "      <th>Opioid</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>Pollution</th>\n",
       "      <th>Fruit</th>\n",
       "      <th>...</th>\n",
       "      <th>Feeling of loneliness</th>\n",
       "      <th>one-person households</th>\n",
       "      <th>Friends</th>\n",
       "      <th>resid_alpha10</th>\n",
       "      <th>resid_alpha11</th>\n",
       "      <th>Insufficient Physical Activity 18+ Age-stand.</th>\n",
       "      <th>Insufficient Physical Activity 18+ crude</th>\n",
       "      <th>Insufficient Physical Activity 11-17 years</th>\n",
       "      <th>salt</th>\n",
       "      <th>Air pollution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEU</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>26.517273</td>\n",
       "      <td>0.287</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.546019</td>\n",
       "      <td>10.9</td>\n",
       "      <td>0.188</td>\n",
       "      <td>45.3</td>\n",
       "      <td>0.473</td>\n",
       "      <td>...</td>\n",
       "      <td>37</td>\n",
       "      <td>39.871429</td>\n",
       "      <td>92.0</td>\n",
       "      <td>-0.395350</td>\n",
       "      <td>0.047194</td>\n",
       "      <td>42.21</td>\n",
       "      <td>45.82</td>\n",
       "      <td>83.69</td>\n",
       "      <td>186.833333</td>\n",
       "      <td>14.114283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DNK</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>25.586903</td>\n",
       "      <td>0.276</td>\n",
       "      <td>3.6</td>\n",
       "      <td>25.925264</td>\n",
       "      <td>9.1</td>\n",
       "      <td>0.169</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.522</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>44.530769</td>\n",
       "      <td>95.3</td>\n",
       "      <td>0.023180</td>\n",
       "      <td>0.239228</td>\n",
       "      <td>28.50</td>\n",
       "      <td>30.76</td>\n",
       "      <td>84.51</td>\n",
       "      <td>160.600000</td>\n",
       "      <td>10.759141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FRA</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>25.916094</td>\n",
       "      <td>0.324</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.805005</td>\n",
       "      <td>11.7</td>\n",
       "      <td>0.254</td>\n",
       "      <td>25.2</td>\n",
       "      <td>0.551</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>34.350000</td>\n",
       "      <td>88.4</td>\n",
       "      <td>-0.475825</td>\n",
       "      <td>-0.107545</td>\n",
       "      <td>29.32</td>\n",
       "      <td>32.25</td>\n",
       "      <td>87.00</td>\n",
       "      <td>145.500000</td>\n",
       "      <td>12.493830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ITA</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>26.307978</td>\n",
       "      <td>0.420</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1.768456</td>\n",
       "      <td>7.6</td>\n",
       "      <td>0.199</td>\n",
       "      <td>48.7</td>\n",
       "      <td>0.758</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>30.466667</td>\n",
       "      <td>90.8</td>\n",
       "      <td>0.340060</td>\n",
       "      <td>-0.307232</td>\n",
       "      <td>41.39</td>\n",
       "      <td>44.81</td>\n",
       "      <td>88.62</td>\n",
       "      <td>190.675000</td>\n",
       "      <td>15.843478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NLD</td>\n",
       "      <td>0.473000</td>\n",
       "      <td>26.261133</td>\n",
       "      <td>0.269</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.404116</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.168</td>\n",
       "      <td>31.3</td>\n",
       "      <td>0.443</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>36.566667</td>\n",
       "      <td>90.1</td>\n",
       "      <td>0.083039</td>\n",
       "      <td>0.035529</td>\n",
       "      <td>27.18</td>\n",
       "      <td>29.45</td>\n",
       "      <td>80.17</td>\n",
       "      <td>171.600000</td>\n",
       "      <td>15.662882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SWE</td>\n",
       "      <td>0.482000</td>\n",
       "      <td>25.818920</td>\n",
       "      <td>0.252</td>\n",
       "      <td>6.6</td>\n",
       "      <td>55.029139</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.104</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.583</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>41.413333</td>\n",
       "      <td>91.8</td>\n",
       "      <td>0.035904</td>\n",
       "      <td>0.048471</td>\n",
       "      <td>23.13</td>\n",
       "      <td>25.22</td>\n",
       "      <td>84.67</td>\n",
       "      <td>141.391304</td>\n",
       "      <td>5.611160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SPA</td>\n",
       "      <td>0.545381</td>\n",
       "      <td>27.162832</td>\n",
       "      <td>0.379</td>\n",
       "      <td>6.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.221</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.840</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>23.692308</td>\n",
       "      <td>94.8</td>\n",
       "      <td>0.425570</td>\n",
       "      <td>-0.200892</td>\n",
       "      <td>26.81</td>\n",
       "      <td>29.57</td>\n",
       "      <td>76.61</td>\n",
       "      <td>178.900000</td>\n",
       "      <td>9.923726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>USA</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>27.602920</td>\n",
       "      <td>0.430</td>\n",
       "      <td>10.7</td>\n",
       "      <td>130.970941</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.105</td>\n",
       "      <td>24.1</td>\n",
       "      <td>0.584</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>22.738448</td>\n",
       "      <td>89.9</td>\n",
       "      <td>-0.036578</td>\n",
       "      <td>0.245248</td>\n",
       "      <td>40.01</td>\n",
       "      <td>42.53</td>\n",
       "      <td>72.05</td>\n",
       "      <td>195.391304</td>\n",
       "      <td>9.520327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  country   Obesity        BMI  Child Obesity  Lack of support MA      Opioid  \\\n",
       "0     DEU  0.600000  26.517273          0.287                 6.0    9.546019   \n",
       "1     DNK  0.510000  25.586903          0.276                 3.6   25.925264   \n",
       "2     FRA  0.490000  25.916094          0.324                 7.7    2.805005   \n",
       "3     ITA  0.460000  26.307978          0.420                 9.2    1.768456   \n",
       "4     NLD  0.473000  26.261133          0.269                 4.8    4.404116   \n",
       "5     SWE  0.482000  25.818920          0.252                 6.6   55.029139   \n",
       "6     SPA  0.545381  27.162832          0.379                 6.6         NaN   \n",
       "7     USA  0.710000  27.602920          0.430                10.7  130.970941   \n",
       "\n",
       "   Alcohol  Smoking  Pollution  Fruit  ...  Feeling of loneliness  \\\n",
       "0     10.9    0.188       45.3  0.473  ...                     37   \n",
       "1      9.1    0.169       30.4  0.522  ...                     25   \n",
       "2     11.7    0.254       25.2  0.551  ...                     45   \n",
       "3      7.6    0.199       48.7  0.758  ...                     47   \n",
       "4      8.3    0.168       31.3  0.443  ...                     35   \n",
       "5      7.1    0.104       18.5  0.583  ...                     30   \n",
       "6      8.6    0.221       27.1  0.840  ...                     40   \n",
       "7      8.9    0.105       24.1  0.584  ...                     30   \n",
       "\n",
       "   one-person households  Friends  resid_alpha10  resid_alpha11  \\\n",
       "0              39.871429     92.0      -0.395350       0.047194   \n",
       "1              44.530769     95.3       0.023180       0.239228   \n",
       "2              34.350000     88.4      -0.475825      -0.107545   \n",
       "3              30.466667     90.8       0.340060      -0.307232   \n",
       "4              36.566667     90.1       0.083039       0.035529   \n",
       "5              41.413333     91.8       0.035904       0.048471   \n",
       "6              23.692308     94.8       0.425570      -0.200892   \n",
       "7              22.738448     89.9      -0.036578       0.245248   \n",
       "\n",
       "   Insufficient Physical Activity 18+ Age-stand.  \\\n",
       "0                                          42.21   \n",
       "1                                          28.50   \n",
       "2                                          29.32   \n",
       "3                                          41.39   \n",
       "4                                          27.18   \n",
       "5                                          23.13   \n",
       "6                                          26.81   \n",
       "7                                          40.01   \n",
       "\n",
       "   Insufficient Physical Activity 18+ crude  \\\n",
       "0                                     45.82   \n",
       "1                                     30.76   \n",
       "2                                     32.25   \n",
       "3                                     44.81   \n",
       "4                                     29.45   \n",
       "5                                     25.22   \n",
       "6                                     29.57   \n",
       "7                                     42.53   \n",
       "\n",
       "   Insufficient Physical Activity 11-17 years        salt  Air pollution  \n",
       "0                                       83.69  186.833333      14.114283  \n",
       "1                                       84.51  160.600000      10.759141  \n",
       "2                                       87.00  145.500000      12.493830  \n",
       "3                                       88.62  190.675000      15.843478  \n",
       "4                                       80.17  171.600000      15.662882  \n",
       "5                                       84.67  141.391304       5.611160  \n",
       "6                                       76.61  178.900000       9.923726  \n",
       "7                                       72.05  195.391304       9.520327  \n",
       "\n",
       "[8 rows x 59 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = '../data_sources/xlsx_files/data_risky_alpha.xlsx'\n",
    "\n",
    "my_sheet  = 'data2'    # sheet name\n",
    "data_risk = read_excel(file_name, sheet_name = my_sheet)\n",
    "data_risk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>de</th>\n",
       "      <th>dk</th>\n",
       "      <th>fr</th>\n",
       "      <th>it</th>\n",
       "      <th>nl</th>\n",
       "      <th>se</th>\n",
       "      <th>sp</th>\n",
       "      <th>us</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Obesity</th>\n",
       "      <td>0.196425</td>\n",
       "      <td>0.165716</td>\n",
       "      <td>0.170822</td>\n",
       "      <td>0.276495</td>\n",
       "      <td>0.198036</td>\n",
       "      <td>0.197081</td>\n",
       "      <td>0.170931</td>\n",
       "      <td>0.328623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Smoking</th>\n",
       "      <td>0.359872</td>\n",
       "      <td>0.402399</td>\n",
       "      <td>0.492596</td>\n",
       "      <td>0.322084</td>\n",
       "      <td>0.357995</td>\n",
       "      <td>0.333276</td>\n",
       "      <td>0.526519</td>\n",
       "      <td>0.578711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phys. Act.</th>\n",
       "      <td>0.050765</td>\n",
       "      <td>0.027652</td>\n",
       "      <td>0.055629</td>\n",
       "      <td>0.103732</td>\n",
       "      <td>0.163662</td>\n",
       "      <td>0.088618</td>\n",
       "      <td>0.041033</td>\n",
       "      <td>0.099483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <td>0.007357</td>\n",
       "      <td>0.007432</td>\n",
       "      <td>0.010157</td>\n",
       "      <td>0.01287</td>\n",
       "      <td>0.008117</td>\n",
       "      <td>0.005634</td>\n",
       "      <td>0.013219</td>\n",
       "      <td>0.027221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diet</th>\n",
       "      <td>376.081096</td>\n",
       "      <td>391.728219</td>\n",
       "      <td>381.142192</td>\n",
       "      <td>347.630137</td>\n",
       "      <td>368.220822</td>\n",
       "      <td>344.921918</td>\n",
       "      <td>336.990959</td>\n",
       "      <td>453.877808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pollution</th>\n",
       "      <td>14.114283</td>\n",
       "      <td>10.759141</td>\n",
       "      <td>12.49383</td>\n",
       "      <td>15.843478</td>\n",
       "      <td>15.662882</td>\n",
       "      <td>5.61116</td>\n",
       "      <td>9.923726</td>\n",
       "      <td>9.520327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    de          dk          fr          it          nl  \\\n",
       "Obesity       0.196425    0.165716    0.170822    0.276495    0.198036   \n",
       "Smoking       0.359872    0.402399    0.492596    0.322084    0.357995   \n",
       "Phys. Act.    0.050765    0.027652    0.055629    0.103732    0.163662   \n",
       "Drug          0.007357    0.007432    0.010157     0.01287    0.008117   \n",
       "Diet        376.081096  391.728219  381.142192  347.630137  368.220822   \n",
       "Pollution    14.114283   10.759141    12.49383   15.843478   15.662882   \n",
       "\n",
       "                    se          sp          us  \n",
       "Obesity       0.197081    0.170931    0.328623  \n",
       "Smoking       0.333276    0.526519    0.578711  \n",
       "Phys. Act.    0.088618    0.041033    0.099483  \n",
       "Drug          0.005634    0.013219    0.027221  \n",
       "Diet        344.921918  336.990959  453.877808  \n",
       "Pollution      5.61116    9.923726    9.520327  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_x = ['country','Obesity','BMI','Child Obesity','Lack of support MA','Opioid','Alcohol','Smoking','Pollution',\n",
    " 'Fruit','Vegetable','Fat diet','Sugar diet','Protein diet','Diabete','Daily calories','Preventive Care',\n",
    " 'leukemia','breast','colon','rectal','lung','stomach','cervical','waiting',\n",
    " 'Cannabis','Ecstasy','Amphetamines','Cocaine','Overdose (per 100 000)',\n",
    " 'rain','temperature','sunshine',\n",
    " 'Daily cigarettes (owid)','Ever smoked (hrs)',\n",
    " 'Prevalence of alcohol disorder','Heavy episodic drinking','Lifetime abstainer','Abstrainer last year',\n",
    " 'Beer consumption','Wine consumption','Spirit consumption','Alcool consumption (Owid)',\n",
    " 'Anxiety','Depression','Bipolar','Eating Disorder','Schizophrenia','Mental and substance use disorder',\n",
    " 'Feeling of loneliness','one-person households','Friends',\n",
    " 'resid_alpha10','resid_alpha11',\n",
    " 'Insufficient Physical Activity 18+ Age-stand.','Insufficient Physical Activity 18+ crude','Insufficient Physical Activity 11-17 years',\n",
    " 'salt','Air pollution']\n",
    "\n",
    "# Aggregation by group of risks\n",
    "# Share of population that consumes a drug\n",
    "# Data from FL\n",
    "DrugL = np.array([0.600343, 0.040283,  0.620790, 0.750047, 0.132868, 0.050934, 0.581897, 8.03])\n",
    "DrugFL = (DrugL/PopL2005)#*100\n",
    "# bad diet: grammes per capita per day\n",
    "DietFL = (data_risk.loc[:,'Sugar diet']/365)*1000 + data_risk.loc[:,'Protein diet'] + data_risk.loc[:,'Fat diet']\n",
    "# Exposure to suspended particules in licrograms per cubic meter (\\mu g/m^3)\n",
    "PollFL = data_risk.loc[:,'Air pollution']\n",
    "\n",
    "name_ss =['Obesity', 'Smoking', 'Phys. Act.', 'Drug', 'Diet', 'Pollution']\n",
    "tableFL = pd.DataFrame(index=name_ss,columns=countries)\n",
    "for cc in range(8):\n",
    "    tt = countries[cc]\n",
    "    for ii in range(3):\n",
    "        nn = name_ss[ii]\n",
    "        tableFL.loc[nn,tt] = dataRF[cc,ii]\n",
    "    tableFL.loc[name_ss[3],tt] = DrugFL[cc]\n",
    "    tableFL.loc[name_ss[4],tt] = DietFL[cc]\n",
    "    tableFL.loc[name_ss[5],tt] = PollFL[cc]\n",
    "tableFL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/h9/sn9_m3953j93s5pv8q78dfvh0000gn/T/ipykernel_62210/352028193.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(countries_plot)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VuD7J7wBkzi+PMuQiFsu9w7uBf2duBjhO/9BYTT8EDhh1iLubJA8Cbq2q9wNnAE8AJpIc1e2/R5JHjzLjYhbJ/dhu1wnzbi8fRbYVfAe4f5L7Jbknc6cg9wIOrqrPAK8BDgTuPbqIixuLv9iq6sokHwSuAr4JXNrtej7wjiR/DtyDue+0unokIRexTO4d+9/cnQd/X5LnV9UdI4h5d3YNcHuSq4Gzqmotnb5p2S8Bb0pyB/BT4OXM/Sv/bd3P8zrm3stpbem0xXKfC9wzyRXM/aV/4gjzLaqqfprkDcAVwPXAl5k7jfr+7v93gLdU1fdGl3JxLnUkSQNKcgMwWVUtfbfS3ca4nOKTJK0xzqAkSU1yBiVJapIFJUlqkgUlSWqSBSXtgiQ/69bk25rkw0n2X+bYFyV5e3d/xZXzkxyZ5Nh5j49Lcmp/6aW1yYKSds2Pq+rIqjocuA14WY+vfSRzqxMAUFXnV9VpPb6+tCZZUNLuuxR4eJL7JjmvWw3680mOWO5Jmfc9XknWdyth7wO8ATihm6GdsGAG9pAkF3VjXJTkkG77WUneluSyJN+I3/ukuyELStoN3bJSz2Ju4dBp4ItVdQTwZwzwVSJVdRvwl8AHuxnaBxcc8nbgvd0YZwNvm7fvgcBTmFu6xhmX7nYsKGnX7JfkKmAGuJG5tRCfArwPoKo+DdyvWzqmT0cB/9Ldf1835g7nVdUdVXUd8ICex5VGbizW4pN68OOFK99n3ve0zLPcJ9/nryq97zLHLWf+689fwXyxLNKa5gxKGtwlzC04TJKnA7dU1Q+WOf4G4HHd/fnvGS23avplzH1pJd1YnxssqrT2WFDS4F4PTCa5hrn3gE5a4fgzgJcnuQxYP2/7Z4BH7bhIYsFzTgZe3I3xQuBVvSSX1gDX4pMkNckZlCSpSRaUJKlJFpQkqUkWlCSpSRaUJKlJFpQkqUkWlCSpSf8PkxbfB2xCR0EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figures where the cost of each risky behaviors is measured as the number of lost years \n",
    "for cc in range(6):\n",
    "    fig, ax = plt.subplots()\n",
    "    clrs = ['grey' if xi < 7 else 'black' for xi in x ]\n",
    "    ax.bar(x,tableFL.loc[name_ss[cc],:],color=clrs)#'grey')\n",
    "    #ax.set_ylabel('# of years lost')\n",
    "    ax.set_xlabel(name_ss[cc])\n",
    "    ax.set_xticklabels(countries_plot)\n",
    "    fig.tight_layout()\n",
    "    plt.savefig('../figures/fig_c1_'+name_ss[cc]+'.eps')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>de</th>\n",
       "      <th>dk</th>\n",
       "      <th>fr</th>\n",
       "      <th>it</th>\n",
       "      <th>nl</th>\n",
       "      <th>se</th>\n",
       "      <th>sp</th>\n",
       "      <th>us</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IndexThep</th>\n",
       "      <td>0.614419</td>\n",
       "      <td>0.6032</td>\n",
       "      <td>0.729204</td>\n",
       "      <td>0.715181</td>\n",
       "      <td>0.72781</td>\n",
       "      <td>0.62461</td>\n",
       "      <td>0.751701</td>\n",
       "      <td>1.034038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 de      dk        fr        it       nl       se        sp  \\\n",
       "IndexThep  0.614419  0.6032  0.729204  0.715181  0.72781  0.62461  0.751701   \n",
       "\n",
       "                 us  \n",
       "IndexThep  1.034038  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_Thep  = ['IndexThep']\n",
    "Index_Thep = pd.DataFrame(index=name_Thep,columns=countries)\n",
    "Index_Thep.loc['IndexThep',:] = tableFL.loc['Obesity',:] + tableFL.loc['Smoking',:] + tableFL.loc['Phys. Act.',:] + tableFL.loc['Drug',:]#/100\n",
    "Index_Thep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "IndexThep = [\n",
    "tableFL.loc['Obesity','de'] + tableFL.loc['Smoking','de'] + tableFL.loc['Phys. Act.','de'] + tableFL.loc['Drug','de'],\n",
    "tableFL.loc['Obesity','dk'] + tableFL.loc['Smoking','dk'] + tableFL.loc['Phys. Act.','dk'] + tableFL.loc['Drug','dk'],\n",
    "tableFL.loc['Obesity','fr'] + tableFL.loc['Smoking','fr'] + tableFL.loc['Phys. Act.','fr'] + tableFL.loc['Drug','fr'],\n",
    "tableFL.loc['Obesity','it'] + tableFL.loc['Smoking','it'] + tableFL.loc['Phys. Act.','it'] + tableFL.loc['Drug','it'],\n",
    "tableFL.loc['Obesity','nl'] + tableFL.loc['Smoking','nl'] + tableFL.loc['Phys. Act.','nl'] + tableFL.loc['Drug','nl'],\n",
    "tableFL.loc['Obesity','se'] + tableFL.loc['Smoking','se'] + tableFL.loc['Phys. Act.','se'] + tableFL.loc['Drug','se'],\n",
    "tableFL.loc['Obesity','sp'] + tableFL.loc['Smoking','sp'] + tableFL.loc['Phys. Act.','sp'] + tableFL.loc['Drug','sp'],\n",
    "tableFL.loc['Obesity','us'] + tableFL.loc['Smoking','us'] + tableFL.loc['Phys. Act.','us'] + tableFL.loc['Drug','us']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From measures per risky behavior to an index of risky behaviors\n",
    "1/ For each risk, make a ranking between countries from the lowest to the highest\n",
    "\n",
    "2/ Take the average of the ranks for each country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rank of each country for each risky behaviors\n",
    "# Order: Obesity EverSmoke PhysAct Poll Diet Drug\n",
    "dataFL = np.array([\n",
    "[4, 4, 3, 6, 5, 3], # de\n",
    "[1, 5, 1, 4, 7, 2], # dk \n",
    "[2, 6, 4, 5, 6, 5], # fr\n",
    "[7, 1, 7, 8, 3, 6], # it\n",
    "[6, 3, 8, 7, 4, 4], # nl\n",
    "[5, 2, 5, 1, 2, 1], # se\n",
    "[3, 7, 2, 3, 1, 7], # sp\n",
    "[8, 8, 6, 2, 8, 8]])# us\n",
    "\n",
    "# Compute the average\n",
    "IndexRank = np.zeros(8)\n",
    "for cc in range(8):\n",
    "    IndexRank[cc] = np.sum(dataFL[cc,:])/6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $\\alpha_{11}$ Transition for good to good health"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope: -1.752709    intercept: 5.143608   R-squared: 0.523838\n"
     ]
    }
   ],
   "source": [
    "slope, intercept, r_value, p_value, std_err = stats.linregress(IndexThep,pars.loc['delta_h2',:])\n",
    "print(\"slope: %f    intercept: %f   R-squared: %f\" % (slope, intercept,r_value**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(IndexThep,pars.loc['delta_h2',:],facecolors='none', edgecolors='b',s=500.0)\n",
    "#sns.regplot(IndexThep,pars.loc['d2',:], color='none', marker=\" \")#color ='blue', marker=\"o\", label=countries)\n",
    "plt.plot(Index_Thep.loc['IndexThep',:], intercept + slope*Index_Thep.loc['IndexThep',:], 'k')\n",
    "plt.ylim(3.3,4.5)\n",
    "plt.ylabel('$\\\\alpha_{11}$')\n",
    "plt.xlabel('Risky behaviors')\n",
    "for x,y,z in zip(IndexThep,pars.loc['delta_h2',:],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n",
    "plt.savefig('../figures/fig3_a11.eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $\\alpha_{10}$ Transition for bad to good health"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>de</th>\n",
       "      <th>dk</th>\n",
       "      <th>fr</th>\n",
       "      <th>it</th>\n",
       "      <th>nl</th>\n",
       "      <th>se</th>\n",
       "      <th>sp</th>\n",
       "      <th>us</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tete</th>\n",
       "      <td>6.036014</td>\n",
       "      <td>6.294224</td>\n",
       "      <td>4.120758</td>\n",
       "      <td>4.912009</td>\n",
       "      <td>5.415615</td>\n",
       "      <td>4.737692</td>\n",
       "      <td>4.137542</td>\n",
       "      <td>6.868274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            de        dk        fr        it        nl        se        sp  \\\n",
       "tete  6.036014  6.294224  4.120758  4.912009  5.415615  4.737692  4.137542   \n",
       "\n",
       "            us  \n",
       "tete  6.868274  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_I  = ['tete']\n",
    "Index_I = pd.DataFrame(index=name_I,columns=countries)\n",
    "Index_I.loc['tete',:] = 70/(1-(tables_s.loc['Smoking',:]+tables_s.loc['Obesity',:]+tables_s.loc['Drug',:]+tables_s.loc['Phys. Act.',:])) - 70\n",
    "Index_I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6.036014239882363,\n",
       " 6.294224160922397,\n",
       " 4.120757790522347,\n",
       " 4.912009173551752,\n",
       " 5.415614732482055,\n",
       " 4.737691699430215,\n",
       " 4.137542103793464,\n",
       " 6.868274020107464]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tete = [Index_I.loc['tete','de'],Index_I.loc['tete','dk'], Index_I.loc['tete','fr'], Index_I.loc['tete','it'], Index_I.loc['tete','nl'], Index_I.loc['tete','se'], Index_I.loc['tete','sp'],Index_I.loc['tete','us']]\n",
    "tete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "de   -1.296156\n",
       "dk   -1.601068\n",
       "fr   -1.094451\n",
       "it   -0.715889\n",
       "nl   -1.264525\n",
       "se   -1.525023\n",
       "sp   -0.005673\n",
       "us   -0.967380\n",
       "Name: delta_h1, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pars.loc['delta_h1',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope: -0.228481    intercept: 0.155666   R-squared: 0.203848\n"
     ]
    }
   ],
   "source": [
    "slope, intercept, r_value, p_value, std_err = stats.linregress(tete,pars.loc['delta_h1',:])\n",
    "print(\"slope: %f    intercept: %f   R-squared: %f\" % (slope, intercept,r_value**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mRKQKFVwG5x9/hHbt3DCaP376yQ2VPfssfPyxBRljIpHfgUZE/pfp9TowNIjtKpJq167N8uXLUVVUlXfffddruR49emQJPJMnTw5xS4umCy+Et9+G0aOhZ0/Xw5k/H/buzTqkduQIvP++S57ZpInbFmD9evfeGBN58jPr7EVVHZrp80xVvS9oLQuiYM0627lzJ82aNcvzGc6NN97I66+/znmFfTIewZKTYdEimDfPJd5MTnaB6MwZOH0a2rSBPn3gzjuhatVwt9YYAwUfOssz0IhInKomikgDVd2d6fsq54a6Ik2opjefOnWK3r17s2rVqlzLVa1alU2bNlG7hGaIVIWff3ZpZcqUgZo1g5MfzRhTOMF8RjNERN4Aqmf6Zc9GapAJpfLly/PJJ59kDLWlp6czblyOLDscOXKEOnXqZBluW716dRhaHB4iEBPj0tXUrm1Bxpjixp//pQ8B8cCbIrJdRHbjfQGmycO55zXnAo+q8vrrr3st26VLlyyB5/nnnw9xa40xJjD8CTS3A5epai2gK/Ap8GVQW1WC9OvXL0vg2bp1q9dyI0aMyBJ4brvtNlKDkRbZGGMCzJ9A8wPQADKmHQ8B7sn9FFNQzZo1yxJ4jh07RmxsbI5y8+bNo3Tp0hmB55JLLuHw4cNhaLExxuTOnxQ0fwIWich6YD0u/cypoLbKZKhYsSKJiYkZn9PT0xk5cmSOobTdu3dzUbZ0xgkJCcTFxYWkncaYounnnyExEb77DlJS3Jq2li2hVSu3Z1Qo5NmjUdWvgVhgAVAe+AmX3t+EQVRUFM8991yWXs/cuXO9lo2Pj88y3DZnzpzQNtYYExZnzsDcudC+PTRsCFOmQFIS7N4Nq1fDsGFQrRrcdptLUBtsYU1BEy7FPXvz+vXr/erJ3HPPPcyYMYMom+ZlTLGxdi384Q9w8cUwciT06gWlSuUsd/SoC0bTp0OHDvCvf+W9Zi3kKWhM0RUbG5ulx3P48GEaNWqUo9wLL7xAqVKlMno8rVu35tixY6FvsDEmIP71L7j5ZnjiCZfy6cYbvQcZgCpVXJaOrVuhRg1o3Ro2bw5OuyzQlADVqlVjx44dGYEnJSWFwYMH5yi3efNmKleunGW4zdcsOGNM0fL88/DPf8K6ddC/v//nlSvncgk+8wz06OH/1uv5YYGmBIqOjmbu3LlZej0zZszwWrZFixZZAs8bb7wR4tYaY/Kyfj08/jisXAn16hWsjgEDXB0DB0Kgd0KxQGMAuO+++7IEnjVr1ngt179//yyBZ+zYsZTE53zGFBWpqe6ZzDPPQP36hatr6FCoWxcCnQfYAo3xqmPHjlkCz48//kiNGjkTQkydOpWoqKiMwNO5c2dOnjwZhhYbUzItWwbly7sZZIUl4p7zTJ8OpwK4iMUCjfFLrVq1OHDgQEbgSU5O5pZbbslRbs2aNVSoUCEj8JQqVYrvvvsuDC02pmSYMcPtXisSmPrq14eOHWHBgsDUBxZoTAGVKVOGRYsWZen1PPnkkznKpaen06hRoyzDbb728THG5E9yMqxZA337Brbe/v0hkPs3WqAxAfPXv/41S+BZsWKF13K9evXKEnieeOIJe85jTAEkJbkFmeXK5Ty2Z88eWrRokfF56tSpTJw4kenTp9OsWTNatWrFwIEDvdYbF+eyCQSKBRoTNN26dcsSeHbv3k358uVzlJswYUKW5zw9e/bkzJkzYWixMZFl9263A21+TJkyhQ0bNrB582ZmzZrltUyjRq7uQP39Z4HGhEz9+vU5efJkRuA5deoU3bp1y1Huvffeo2zZshmBp1KlSvzwww9haLExRVtaGkT7k7Eyk1atWnHbbbfx6quvEu3j5OhoSE8PQAM9whpoRKS/iGwVkXQR8ZnWQESu9+yF852IjMv0fRURWSEi33p+Vg5Ny00glCtXjhUrVmTZGG7ChAk5yh0/fpy6detmGW775JNPQt9gY4qYSpXgyBHvx6Kjo0nPFC3OjRK88847DB8+nMTEROLi4rxuN3L0qNtaPVATDMLdo0kCbsHtceOViJQCngd6As2AQSLSzHN4HPChqjYCPvR8NhFKRHjssceyDLctWbLEa9muXbtmCTzTpk0LbWONKQJat4YNG7wPcVWvXp1Dhw5x5MgRkpOTWbZsGenp6fzwww907dqVp556imPHjnldjrB+PbRpE7h2hjXQqOo2Vd2eR7F2wHequktVz+KySJ/LHn0TcC518Vzg5kC279Qpl3Tu7rvdw7E6dVyiussvh3vvhXnzXJZUEzx9+vTJEni++eYbr+VGjx6dJfAMGDCAlJSUELfWmNCqUcOl/U9KynmsdOnSTJgwgfbt23PDDTfQpEkT0tLSuP3222nZsiVt27Zl9OjRVKpUKce5n37qMj8HSpHI3iwinwBjVDVHSmUR6Qdcr6pDPZ/vANqr6ggROaaqlTKV/UVVvQ6ficgwYBhA3bp1477//nuf7Tl50iWle/FF6NTJ5f+JjXX72QPs3etmZCxf7iL//ffDuHGh29vB/J8TJ07QvXt31q5dm2u5OnXqkJCQQPXq1UPUMmNCY8IE+OUXt9AyEFJS3Fqa99+HTJPWgCKcvVlEVopIkpeXv3vaeBslzHd0VNXZqhqvqvExMTE+y332meuO/vST65IuWeICyRVXuN7MxRe7xUwjR8J777m9HJKSXI9n/fr8tsoUVoUKFfjiiy8yejxpaWmMGjUqR7l9+/ZRo0aNLL2edevWhb7BxgTYsGFudGXfvsDUN2cONG6cM8gUSuZhiXC9gE+AeB/HOgDvZ/o8Hhjveb8dqOl5XxPY7s/vi4uLU2+WLVONiVFdssTrYZ/S01XnzXPnrlqVv3NN8M2bN09xf5zk+vr3v/8d7qYaUyCPPaZ6/fXu36LC2LtXtVo11c2bvR8HErQA/8aHezKAP74CGolIAxEpAwwE3vYcexsY4nk/BPD+5NgP69fDnXfC0qXQp0/+zhWBQYNg/nzo1w927ChoK0wwDBo0KMt/9Bs3bvRa7u67787S4xk6dChpaWmhbawxBTB+vJsp9uijBV/7cvy4yzAwZozb6jmgChKdAvUC+gL7gGTgIJ6eC1ALWJ6pXC9gB7ATeDjT91Vxs82+9fys4s/vzd6jOXNGtUUL1f/+17+on5vp01WvvFI1NbXwdZnQOHLkiDZr1izPHk+zZs30yJEj4W6uMV4dOuT+HXvgAdXTp/N37s6dqrGxqiNH5t4rIhJ7NKq6WFXrqOp5qlpdVXt4vt+vqr0ylVuuqo1VtaGqTsr0/RFVvVZVG3l+Hi1IO2bMcHs4BCL76fDhEBUFr7xS+LpMaFSpUoWtW7dm/E+RmprK0KFDc5T7+uuvqVq1apZez+ZgbUloTD7FxMCqVe5ZTVwcfPRR3r2b336DadOgXTv379+0aYFbO5NZkZh1Fmrx8fGakOAmuKWnuwdfr70WuOl8774LjzwCCQnBuWkm9F588UXuvvvuPMvNmzePQYMGhaBFxninCgsXwmOPuT96Bw2C+Hho2hTKlIFjx2DjRjfxaf58uPJKePppaNIk77qL7Kyzou6zz9xeDu3a+S4zffp0mjZtym1+dnl69HCrdb3NbTeRaejQoVmGAnxNp7711luz9HhGjx5NSfxjzoSPiNsl8+uv4bnn3LOXp55ySzXatnXPoN94w60LTEx0z6X9CTKFalNJ/J8gc49m6lS3Lmb6dN/lmzRpwrvvvkuDBg0yvktNTfWZJwjgjjvg6qvhrrsC1WpTlP3000+0b9+evXv35lpu9uzZDB48mPPOOy9ELTMmcKxHU0AbN7oo78u9997Lrl276NOnDxUrVmTYsGF0796dwYMH51pvbKyr25QMNWrU4Pvvv8/o8Zw9e5bf//73OcoNGzaM888/HxGhTJky/O1vf+Pnn38OQ4uNCZ0SH2iOHYNq1XwfnzVrFrVq1eLjjz9m9OjRJCYmsmTJEubNm5drvdWqudW6pmQqXbo0CxcuzDLctnr1avpkmjufkpLC448/TkxMTMZQ2x//+EefaXaMiVQlPtCUKgVekpf61KdPH8qWLZtnudTU/KfvNsVbp06dWLJkSUbg2b17N8OHD89S5j//+Q9NmzbNCDzdu3fno48+suc8JqKV+EDToAHkZ0t7bxt3efPtty5fkDG+1K9fn+eeey4j8Bw7dozJkydz4YUXZpRZsWIF1157bcbGcE2bNuWVV16xhKEmopT4QBMX56YhB1pioptSaIy/KlasyLhx4zh+/HjGc55XX32V5s2bZ5T55ptvGDJkCGXKlEFEuOCCC5g0aRLHjh0LX8ONyUOJDzTXXAMrV7qMzYFy6BB8+aVLvmlMQZUuXZrbbruNpKQkVN3GcJ988gnXX399RplTp07xyCOPULly5Yzhtvvuu49du3aFseXGZFXipzcD3HILdO/u9pgJhP/3/2DnTnjppcDUZ4wv3377LU899RQvvviizzK9e/dm/PjxdLS/fEwhFXR6swUaYN06uPlm2LIl9xlo/ti71w3HrVoFzZrlXd6YQDp69CjPPfcckydPzti6N7tWrVoxfvx4+vfvT6lSpULcQhPJbB1NIbRvD7fe6vZ1yLTFdr6lpMAf/wijRlmQMeFRpUoVJkyYwOnTp1FVkpOTeemll7j00kszymzevJlBgwYRHR2NiFC1alWmTp3KiRMnwthyU5xZj8bj9GmXOqZJE5g50017zo+UFJcN4Ndf3WZppUsHsMEmYh075rag2LEDkpNduqPmzd3meuXKhb49qsrKlSuZPHkyH3/8sc9yI0eOZMyYMVx88cUhbJ0p6qxHU0hly8KyZW6q83XXwZ49/p/7zTfQpYsLVosWWZAp6VJT4fXXoWtXl0/q0UfdLMTvvoM1a1yG75gYt3eRPxl2A0lEuO666zLW5qgqSUlJOTJdTJ8+nbp162ZMMPjd735H9j/OjPFbQfYWiPSXrx02VVVTUlSffFK1alXVP/9ZdccOn0U1KUl1+HBX9vnnVdPSfJc1JcOmTW5fjyuvVF24UDU52Xu548dVZ8xQvewy1RtvVN2/P7TtzM3Bgwf1oYce0qioKJ9781x++eX65ptvapr9R1+iUMD9aGzozIfvv4dZs9zMsQoV3AP+WrXcsb173V+oKSlw993u2U7t2iFouCnSXnvNPZ+bMsU9q/Nni4izZ+Hvf4cXXoDFi13K9qLmzJkzzJkzh8mTJ/tMGlqjRg3Gjx/P0KFDKReOMUETEjbrLB/8CTTnpKW5Vf6JiXDwoNvfoUYNF3gaNnSfjVmwAP7yF/jgA/cMJr/efReGDIHly4v+Ql9VZfny5UyePJnPPvvMZ7mxY8cyevRoatas6TnPbcvx0Ufu/6d9+9zkmypVXGLbDh3ghhvAElsXXRZo8iE/gcaYvOzc6WYufvQRtGpV8HoWLYK//hU2bYILLghc+0Jh48aNTJkyhYULF/osU7HiQCpVGseAAa2Jj3fpn6Ki3ALn9evdwumtW+Gee2DcuPBMljC5s0CTDxZoTKCouof+N90Eo0cXvr4hQ6BSJfjnPwtfVzj9+OOPTJs2jalTp/osExMTw0MPPcTIkSOJ8gwNbN/udob86iuYO7doDiWWZAUNNGF9KA/0B7YC6UC8jzIXAx8D2zxl/5Tp2ETgR2Cj59XLn9+b22QAY/JjzRrVxo1VU1MDU9/hw6qVKrmfkW7nTtV69VQffVT1l19O6rRp07RGjRo+JxgAOnToUD1x4oQuXqwaE6O6ZEm4r8JkRgEnA4Q70DQFLgM+ySXQ1ARiPe8rADuAZvp/gWZMfn+vBRoTKLfeqjptWv7PGzJkiL7++utej/3hD27mYyT75RfVSy5xszG9SUtL0ylTpuQadACNju6gb7yxO5RNN7koaKAJ66NsVd2mqtvzKHNAVdd73p/A9WxsjpcJO1X38L9fv8DW+/vfu3oj2ejRbgH0/fd7Px4VFcWDDz6Y5R+jN998M0e51NQv6NevQcZ6nooVK/Lpp58GufUm0CJqzpSI1AfaAusyfT1CRDaLyMsiUjk8LTMl0Q8/uM3tcpvavmfPHpo2bcrdd99N8+bN6d69O6dPn8613rg4NytLI/Tx6Zo18Mkn8NRT+Tuvb9++WQLPpk2baNSoUZYyv/76K1dddVVG4BERZs+efW70wxRRQQ80IrJSRJK8vG7KZz0XAIuAUar6q+frmUBDoA1wAHgml/OHiUiCiCQcPny4YBdjTCa7dkGmFGI+ffvttwwfPpytW7dSqVIlFi1alGv5iy5y034jdYuZ6dNhzJjCz5xr1aoVO3bsYNs2pXp15YcfDnHTTTn/2bjnnnsyNoYTEf70pz/5TChqwiPogUZVu6lqCy+vJf7WISKlcUHmNVXN6F+r6kFVTVPVdODfQLtc2jFbVeNVNT4mJqYwl2QM4NZY+ZNuqEGDBrRp0waAuLg49viR3yg6On9bjBcVv/zihv3uuCP3chMnTsx1RlpmTZq4JLVffRXDW2+9ldHjOXv2LA899FCO8tOnT6ds2bIZgadbt27s37+/IJdjAqTID52JiAAvAdtU9dlsx2pm+tgXSApl20zJVrEiHDmSd7nzMq1ALFWqFKl5RJCzZ+G331xGikiTkOAShmbajTogrrkGvvgi63elS5dm0qRJWYbbXnvttRznfvjhh9SuXTsj8MydO5ejR48GtoEmV2ENNCLSV0T2AR2Ad0Tkfc/3tURkuadYR+AO4BoR2eh59fIce0pEtojIZqArEICVDMb4p3lzlzUi0KM0W7fCJZfA+ecHtt5Q2LABYmO9H5s0aRKXXXYZ3bp1Y/t2Nwdo586dXH/99cTFxdG5c2e++eYbr+fGxbm683LrrbdmCTxffvkltbM9RPvDH/5A1apVadGiBffeey+vvfYa33//fb6u0+RPdDh/uaouBhZ7+X4/0Mvzfg3gNWuUqubRQTcmeMqWhaZNYe1auPrqwNX76afQzucgcNF25Ih7xpRdYmIiCxYsYMOGDaSmphIbG0tcXBzDhg1j1qxZNGrUiHXr1nH//ffz0Ucf5Tj/oov86z1md/nll7Nv376Mz6dPn+arr75izZo1rFmzhvnz5/PCCy8AcPHFF9OpUyc6depE586dad68ecZCUlM4YQ00xkS6wYNh9mzfgaZ+/fokJf3fiO6YMWNyrU/VJdicMSOAjQyhUqW8bx64evVq+vbtm5Fws0+fPpw5c4bPP/+c/v37Z5RLTk72Wm9aWv73iPKmbNmydOnShS5dunjqTSMpKYnVq1ezZs0aVq1axfz58wGoVKkSHTt2zAg88fHxWYZBjf8s0BhTCEOGwOOPuz2JmjQpfH1vveXyf111VeHrCoc6dVzeMm8kWzrr9PR0KlWqxMaNG/Osd/duV3eglSpVitatW9O6dWtGjBiBqrJnzx7WrFmTEXzeeecdwD1ru/zyy+ncuTOdOnXiyiuvpFKlSoFvVDFk/UJjCqFSJRdo7rzT/dVdGEePuk3RZs70b4uBoig+3q0Byq5Lly4sXryY06dPc+LECZYuXUq5cuVo0KABr7/+OkDG2hlvEhPdc5pgExEaNGjAHXfcwezZs/n66685fPgwb731Fg888AApKSk8/fTT9O7dmypVqmQEqAULFmQZojNZWVJNYwopPR2uv96tqXn++YIFidOnXYr8tm3Bz1m/RVJysut5fPmly86c2aRJk3jllVeoV68ederUoVmzZvzud7/jvvvu48CBA6SkpDBw4EAmTJiQ5bz0dPcs7D//KRpJNn/77TfWrVuX0ev54osvOHnyJOCGSs8NtXXq1ImmTZvm6MlFMsvenA8WaEyg/fordO/uhs+efx7Kl/f/3AMHYNAgl2HglVcC8ywinMaMcdfw5JOBqW/FClfnxo1Fs6eXmprK5s2bM4baVq9ezcGDBwGoWrVqluc8sbGxlClTJswtLjgLNPlggcYEw8mTbuhrzRq3Or5nz9w3xktOhldfhfHj4b77YMKEyA8yAHv2uCG0L76AbBlk8u3sWTcDb+xYuO22gDQv6FSVnTt3ZnnOs2PHDsBNRmjfvn1G4Lniiiu4MNCLjoLIAk0+WKAxwbRsGTzyCJw65f5xbNfOrbk5/3w4ccJtbPb5527r5+bN4emnfa89iVTTpsEbb7jN4ArzB/wjj7iezNKlRbM346+DBw/y2WefZQSeDRs2kJaWRlRUFK1bt84YauvUqVPGjqRFkQWafLBAY4JN1f1Fv2SJe5C9fbvrwZQvDy1auL/4BwwIzEy1oig9HW65xaXoee21ggWb556DZ55xQbkI/9tbICdPnmTt2rUZvZ61a9fy22+/AdCwYcMsgadx48ZF5jmPBZp8sEBjTPCdOQMDB8LPP7sH+f4Oo5044ba0XrHCvbJPKiiOUlJS2LBhQ8ZC0tWrV/Pzzz8DbifSzBMM2rZtS3R0eFamWKDJBws0xoRGejr861/wxBNuCvh997n0Ot4cP+4mQzzzDFx7LTz7rMsnVxKpKjt27MgywWDXrl0AlC9fniuuuCIj+LRv354LCpsq208WaPLBAo0xobVnj5uNN2eOm10XF+d6KqVKwaFDbnhx82Y3TfyBB6Bjx3C3uOjZv39/luc8mzZtIj09nVKlShEbG5sReDp27MhF3vIABYAFmnywQGNMeCQnu8kQiYmwb59b5Fq1qls/FBcHlW3rQr8dP36ctWvXZgSedevWZezD07hx44yhts6dO3PJJZcE5DmPBZp8sEBjjCluzp49S2JiYsZQ22effZaxHULNmjUzJhcMGTKEigUck7RAkw8WaIwxxV16ejrffPNNluc8e/fu5ciRI1QuYNfRAk0+WKApuN9+c9NNExNh505ISXEbdLVu7daLtGgR2esdjCnOfvrpJ2rUqFHg8wsaaCyppvHL3r0wahRcfDFMnAg//eQWGXbqBPXrw6pVcNNN0KaNS5ufkhLe9hpjcipMkCkM2ybA5Orc/iiPPgp33eVSwNer571serpbCT5lijtnzhxo2TKkzTXGFEEWaIxPaWkuuCQluR5Ls2a5l4+Kgm7d3BqIl192+7zPmQO9e4ekucaYIsqGzoxP99/vpqD6E2QyE3EBatkyt0hv1argtdEYU/RZoDFevfkmfPyx2/ExPynvM2vf3q30HjzYrfo2xpRMYQ00ItJfRLaKSLqI+JzJICJ7RGSLiGwUkYRM31cRkRUi8q3npy33CoATJ2DECJefqrCZLa6/Hnr0gIcfDkzbjDGRJ9w9miTgFuBTP8p2VdU22abWjQM+VNVGwIeez6aQXn0VOnQIXBqQSZNcBt9ffglMfcaYyBLWQKOq21R1eyGquAmY63k/F7i50I0yvPCCez4TKDExbkLAK68Erk5jTOQId4/GXwp8ICKJIjIs0/fVVfUAgOenz0xyIjJMRBJEJOHw4cNBbm7kOnbMLcS8+urA1tu7N3zqT7/VmADatw+eegp+9zu49FL3R0/Nmq63PnIkvPOOm11pgivogUZEVopIkpfXTfmopqOqxgI9geEi0iW/7VDV2aoar6rxMTEx+T29xFi/3q3y97al8KlTp+jduzetW7emRYsWLFy4kMTERK666iri4uLo0aMHBw4c8FpvXJzLJmBMKOzeDf36QatWsGuXe79sGWzdCgkJbjj33OLjSy+Ff//brRkzwRH0dTSq2i0Adez3/DwkIouBdrjnOgdFpKaqHhCRmsChwv6uku7AAahb1/ux9957j1q1avHOO+8ALntsz549WbJkCTExMSxcuJCHH36Yl19+Oce5deu6uo0JthdecJNPxoxx67i8TWipXdv12seOhXXr3NYE//sfzJ0LtWqFusXFX5FfsCki5YEoVT3hed8deNxz+G1gCDDF83NJeFpZfKj6zlXWsmVLxowZw4MPPsgNN9xA5cqVSUpK4rrrrgMgLS3N537nIvYXowm+Rx+FRYtgzRr/t8lu397l7/v7311KpY8/9p39whRMWAONiPQF/gXEAO+IyEZV7SEitYAXVbUXUB1Y7NlLIRqYp6rveaqYAvxPRO4C9gL9Q34RxcxFF8H+/d6PNW7cmMTERJYvX8748eO57rrraN68OV988UWe9e7f78bHjQmWWbNckFm1Kv//rUVHu2G0ypXddPzExIKvHzM5WfZmk8Xhw27M+pdfXEqZzPbv30+VKlU4//zzeeutt5g9ezY7duzgv//9Lx06dCAlJYUdO3bQvHnzHPW+8Yabdfb22yG6EFOi7Nrlsod/9hlcdlnh6rr9dqhWDaZNC0jTipWCZm8u8kNnJrTOzcpZt86tpclsy5YtjB07lqioKEqXLs3MmTOJjo5m5MiRHD9+nNTUVEaNGuU10HzwgW3Pa4LnL3+Bv/618EEGYPp0l3Lp7rvBy3/KpgCsR2NymDoVtmxxD0YD4fhxt5XAtm0Qpizlphj7/nu3ZcXevYEb7vrb3+DIEXjuucDUV1zYfjQmYO68E5Yvd8EmECZPhhtusCBjgmPePBg4MO8gM3HiRKZOncrVV19NXn9o3n23y5Bha2wCwwKNyaFqVRcc/vAHOHu2cHWtW+emmE6dGoiWGZPTunVw1VWBrbNOHfecZnth8paYDBZojFd33eXWvgweDKmpBatjxw645RY3G6h69cC2z5hzNm2Ctm29H5s0aRKXXXYZ3bp1Y3u2qJGens6QIUN45JFHvJ4bGwsbNwa4sSWUBRrjlYgbkjh+3A17+Zry7Mu777oFcX//O9x8czBaaIxz7JjrhWeXmJjIggUL2LBhA2+++SZfffVVxrHU1FRuu+02GjduzN///nev9VapYttbBIoFGuNT2bKwZImbNtqmjZuN8+uvuZ+zdavrBd1/vxvjvvPOkDTVlGDR0ZCSkvP71atX07dvX8qVK8eFF15Inz59Mo7dc889tGjRgodz2b8iNdXVbQrPAo3JVZky8Pjj8P77brV1vXowYAA8/TQsXeq+X7AAxo1z05e7dYMGDWDzZreVszHB1qABfPut92PiI83FlVdeyccff8yZM2d81rtjh6vbFJ4FGuOXtm1dLqitW10m5n373LOXZ55xizHLlYNHHnFTTR97DCpUCHeLTUkRF+cSZWbXpUsXFi9ezOnTpzlx4gRLly7NOHbXXXfRq1cv+vfvT6qXh5Bpae75TGxsEBtegljH0ORLrVpuaGzw4HC3xBinRw/Xwx41Kuv3sbGxDBgwgDZt2lCvXj06d+6c5fif//xnjh8/zh133MFrr71GVKZUGO+953KlVakSggsoAWzBpjEmoqWmugXBS5f6nn2WX716Qf/+9owxO1uwaYwpkaKjYfx4GDEiMAssly5162cGDix8XcaxQGOMiXj33ecCzlNPFa6eAwdcXS+/7GZdmsCwZzTGmIgXFQX//S907uxS/d97b/7rOHAArrvOBZpAZxoo6axHY4wpFurWhY8+cumO7rorf4st33kHLr8cBg2Chx4KXhtLKgs0xphio2FD2LDBrf9q1gwmTYKffvJeNj3dzS674QYYOdItMH74Yd87zJqCs6EzY0yxUqECzJwJ99wDM2ZA48ZQu7abkVa1qssisGMHrF/vAtP997s1YuXKhbvlxZdNbzbGFGspKfD11y755rFjULq0W/EfF2fbi+eX7bBpjDFelC4NrVu7lwkPe0ZjjDEmqMIaaESkv4hsFZF0EfHaHRORy0RkY6bXryIyynNsooj8mOlYr5BegDHGmDyFe+gsCbgFeMFXAVXdDrQBEJFSwI/A4kxF/qGqtn+jMcYUUWENNKq6DXyn8vbiWmCnqn4ftEYZY4wJqHD3aPJrIDA/23cjRGQwkAD8RVV/8XaiiAwDhnk+nhSR7UA14OdgNTbM7NoiV3G+vuJ8bVC8r68aUK8gJwZ9erOIrARqeDn0sKou8ZT5BBijqj7nHItIGWA/0FxVD3q+q467qQo8AdRU1T/mo20JBZmqFwns2iJXcb6+4nxtULyvrzDXFvQejap2C1BVPYH154KMp+6M9yLyb2BZgH6XMcaYAImk6c2DyDZsJiI1M33si5tcYIwxpggJ9/TmviKyD+gAvCMi73u+ryUiyzOVKwdcB7yZrYqnRGSLiGwGugKj89mE2QVvfZFn1xa5ivP1Fedrg+J9fQW+thKZgsYYY0zoRNLQmTHGmAhkgcYYY0xQlYhAIyKlRGSDiOSYlSbOdBH5TkQ2i0hsONpYUHlc29UicjxTip4J4WhjQYnIHs8zuI0ikmPqezG4d3ldX8TePxGpJCJviMg3IrJNRDpkOx6x986Pa4vk++Yz5VemMvm+d5G2YLOg/gRsAy70cqwn0Mjzag/M9PyMFLldG8BqVb0hhO0JtK6q6msBXKTfO8j9+iBy798/gfdUtZ9nDVz23V4i+d7ldW0QoffNj5RfUIB7V+x7NCJSB+gNvOijyE3AK+qsBSplmzZdZPlxbcVdxN674kxELgS6AC8BqOpZVT2WrVhE3js/r6248JXyK9/3rtgHGmAa8Fcg3cfx2sAPmT7v83wXCaaR+7UBdBCRTSLyrog0D02zAkaBD0Qk0ZNCKLtIvneQ9/VBZN6/S4DDwH88w7ovikj5bGUi9d75c20QmfctO28pv6AA965YBxoRuQE4pKqJuRXz8l2Rn/Pt57WtB+qpamvgX8BboWhbAHVU1VhcV324iHTJdjwi710meV1fpN6/aCAWmKmqbYFTwLhsZSL13vlzbZF63zJ4hgT7AK97O+zlu1zvXbEONEBHoI+I7AEWANeIyKvZyuwDLs70uQ4up1pRl+e1qeqvqnrS8345UFpEqoW8pQWkqvs9Pw/hxonbZSsSqfcOyPv6Ivj+7QP2qeo6z+c3cP84Zy8Tifcuz2uL4PuWWY6UX5nk+94V60CjquNVtY6q1sd1Az9S1duzFXsbGOyZSXEFcFxVD4S6rfnlz7WJSA0RtweDiLTD3e8jIW9sAYhIeRGpcO490J2cKYYi8t6Bf9cXqfdPVX8CfhCRyzxfXQt8na1YRN47f64tUu9bNjlSfmWS73tXUmadZSEi9wKo6ixgOdAL+A74DbgzjE0rtGzX1g+4T0RSgdPAQI2cVBDVgcWe/1+jgXmq+l4xunf+XF8k378HgNc8QzC7gDuL0b3L69oi+b5lTvl1T6bvCnXvLAWNMcaYoCrWQ2fGGGPCzwKNMcaYoLJAY4wxJqgs0BhjjAkqCzTGGGOCygKNMUEgImme7LdbPalI/iwiUdnKLBGRL7J9N1FExnjeNxeRHSJSNtPxd0RkYGiuwpjAsEBjTHCcVtU2qtoctyahF/C3cwdFpBJuRXklEWngrQJV3Yrbvvxhzzk3A6VVdUFwm25MYFmgMSbIPClmhgEjzq0YB34HLMWlD8qth/I40F9E2gBTgOEAInKV/N+eIRvOZRkwpiiyQGNMCKjqLtz/bxd5vjqX4mO+572v834DxgCfAgtU9VvPoTHAcFVtA3TGrUA3pkiyQGNM6JzLf1UduBRYo6o7gFQRaeHrJFVdChwDZmT6+jPgWREZCVRS1dSgtdqYQrJAY0wIiMglQBpwCBgAVAZ2e7Jv1yf34TNwew5l7DukqlOAoUBZYK2INAl8q40JDAs0xgSZiMQAs4DnPMkVBwHXq2p9T/btOPIONNnrbKiqW1T1SSABsEBjiqwSmb3ZmBAoKyIbgdJAKvBf3FBXfaAusPZcQVXdLSK/isi5fdcfEZFRmY7X8VL/KBHpiuslfQ28G4yLMCYQLHuzMcaYoLKhM2OMMUFlgcYYY0xQWaAxxhgTVBZojDHGBJUFGmOMMUFlgcYYY0xQWaAxxhgTVP8fb+cWK0LUzTUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(tete,pars.loc['delta_h1',:],facecolors='none', edgecolors='b',s=500.0)\n",
    "plt.plot(Index_I.loc['tete',:], intercept + slope*Index_I.loc['tete',:], 'k')\n",
    "plt.ylabel('$\\\\alpha_{10}$')\n",
    "plt.xlabel('DALYs')\n",
    "plt.ylim(-1.75,0.15)\n",
    "for x,y,z in zip(tete,pars.loc['delta_h1',:],countries):\n",
    "    plt.annotate(z,xy=(x, y),horizontalalignment='center', verticalalignment='center')\n",
    "plt.savefig('../figures/fig3_a10.eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/h9/sn9_m3953j93s5pv8q78dfvh0000gn/T/ipykernel_62210/2763819837.py:6: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(countries_plot)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(len(countries))\n",
    "fig, ax = plt.subplots()\n",
    "clrs = ['grey' if xi < 7 else 'black' for xi in x ]\n",
    "ax.bar(x,tete,color=clrs)#'grey')#label='Total')\n",
    "ax.set_ylabel('# of years lost')\n",
    "ax.set_xticklabels(countries_plot)\n",
    "fig.tight_layout()\n",
    "plt.savefig('../figures/fig_c2_DALYs.eps')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "from bad to good / DALYs withoy diet\n",
      "[-0.3333333333333334, 0.4197530864197531]\n",
      "from good to good / Continuous Index\n",
      "[-0.8095238095238096, 0.014902667686230067]\n"
     ]
    }
   ],
   "source": [
    "print(\"from bad to good / DALYs withoy diet\")\n",
    "res01_spear_rho, res01_spear_pv = spearmanr(tete[0:8], pars.loc['delta_h1',['de','dk','fr','it','nl','se','sp','us']])\n",
    "print([res01_spear_rho, res01_spear_pv])\n",
    "\n",
    "print(\"from good to good / Continuous Index\")\n",
    "mm = np.array(IndexThep)\n",
    "mm = mm[[0,1,2,3,4,5,6,7]]\n",
    "res02_spear_rho, res02_spear_pv = spearmanr(mm, pars.loc['delta_h2',['de','dk','fr','it','nl','se','sp','us']])\n",
    "print([res02_spear_rho, res02_spear_pv])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
